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SSRN 2024年

作者： Zhu, Hongjun Liu, Binghong Zhou, Sen Zhang, Xiaoyan Xin, Liu Chen, Kuo Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China Chongqing Academy of Metrology and Quality Inspection Chongqing401121 China School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland1010 New Zealand Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

This study presents a computational methodology for developing a multi-ball system that automatically aligns during assembly processes. The contributions of this work are threefold: Firstly, a comprehensive error analysis of the multi-ball system is performed. It is found that temperature and pressure-induced deformations should not be ignored when micrometer-level precision is required. Secondly, a novel process flow for designing the multi-ball pitch artifact is introduced, which enables optimization decision-making. Lastly, a series of experiments are conducted on standard bearing balls (grade G20). The resulting artifact meets the Class 1 standards. It is reasonable to assume that, by using balls of grade G3 precision, the multi-ball artifact can achieve accuracy at the sub-micrometer level. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Automatic Deep Active Contour Model Based on Local Image Characteristics

Automatic Deep Active Contour Model Based on Local Image Cha...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haijun You Kai Li Junying Chen Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education School of Software Engineering South China University of Technology Guangzhou China Department of Ultrasound Third Affiliated Hospital of Sun Yat-sen University Guangzhou China

In recent years, convolutional neural networks have made remarkable achievements in medical image segmentation. Unclear margins and fuzzy boundary regions are formed surrounding the thyroid gland area under the influence of artifacts and speckles, which increases the difficulty of thyroid ultrasound image segmentation. However, building an effective CNN model usually requires massive computing resources, long training time, and large amounts of carefully labeled data. In this work, we develop a fast, compact, accurate and robust image segmentation model for automatic thyroid segmentation in medical ultrasound images. We propose an automatic deep active contour (DAC) model based on local image characteristics, which utilizes the deep neural network model to obtain the initial contour and constructs external energy items based on the local image characteristics of contour points. Compared with the deep neural network model, the DAC model requires less training data, and the segmentation performance of the DAC model on a small dataset can reach that of the deep neural network model on a larger dataset. The proposed DAC model can be used for automatic thyroid segmentation in medical ultrasound images, which does not require massive computing resources, long training time, and large amounts of carefully labeled data.

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Look from Coarse to Fine: A Hierarchical Lane Detection Network

Look from Coarse to Fine: A Hierarchical Lane Detection Netw...

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Artificial Intelligence Innovation (ICAII), International Conference on

作者： Xichen Yu Yanduo Zhang Tao Lu Xun Li Jun Chang Hubei Provincial Key Laboratory of Intelligent Robot School of Computer Science and Engineering & School of Artificial Intelligence Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and School of Computer Science Wuhan University Wuhan China

Lane detection involves identifying a high-level semantic identifier with a slender structure, which requires both high-level feature detection for existence and low-level features for shape and position determination. The objective of this study is to introduce a framework called hierarchical lane detection (HLDNet), which from coarse to fine established stronger relationships between the global and local lanes to achieve stable and accurate lane detection. Specifically, we reframe the existing lane identification method by expressing lanes as a combination of lane instances (for existence detection) and lane point sets (for shape detection). Our proposed Lane IoU loss improves the model performance by regressing the lane instance as a whole. To enhance stability, we capture the complete lane with high-level features using multiple receptive fields. Additionally, our model adaptively fuses high- and low-level features, propagates key features between channels, and further enhances lane local feature representation. Our experimental results show significant improvements in the stability and accuracy of lane detection across multiple benchmark datasets and can run in realtime at a speed of 91 FPS.

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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Four Diagnostic Description Extraction in Clinical Records of Traditional Chinese Medicine with Batch Data Oversampling 21

Four Diagnostic Description Extraction in Clinical Records o...

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21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Wang, Yaqiang Li, Kailun Jiang, Yongguang Shu, Hongping College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Preclinical Medicine Chengdu University of Traditional Chinese Medicine China

Four diagnostic description extraction in clinical records of traditional Chinese medicine (TCM) has important application value in improving the quality and efficiency of TCM clinical syndrome differentiation and treatment. However, the extraction task is yet to be explored, and imbalanced class distribution is one of the key challenges of this task. As a first exploration of this task, we firstly constructed a TCM clinical four diagnostic description extraction corpus and then solved the domain adaptation by fine-tuning the general domain pre-trained language model based on unlabeled TCM clinical records. At last, we trained our proposed four diagnostic description extraction model by utilizing a small labeled dataset through a well-designed batch data oversampling algorithm. The experimental results show that the performance of the proposed method in this paper is better than that of the compared methods, and the proposed method improves the extraction performance F1 score of the rare class by 2.13% on average. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Extraction

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Bingshu Wang Jia Lin Xiaodong Zhuang Guanghui Zhang C.L. Philip Chen School of Software Northwestern Polytechnical University Xi'an China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy.

关键词： Training Learning systems Dimensionality reduction Accuracy Artificial neural networks Feature extraction Road safety Cybernetics Principal component analysis Image classification

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GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

arXiv

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a "teacher" to create guidance prompts, paired with a smaller "student" model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation. Our code is publicly available at https://***/Zoeyyao27/GKT © 2024, CC BY.

关键词： Distillation

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Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Service Localization and User Satisfaction Enabled Multi-Tier Computing IoT System

Service Localization and User Satisfaction Enabled Multi-Tie...

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International Conference on Distributed Computing Systems Workshop

作者： Chenyu Gong Mulei Ma Liekang Zeng Yang Yang Liantao Wu Zening Liu IoT Thrust and Research Center for Digital World with Intelligent Things HKUST(Guangzhou) Guangzhou China Peng Cheng Laboratory Shenzhen China Terminus Group Beijing China Software Engineering Institute East China Normal University Shanghai China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350354713

ISBN: (纸本)9798350354720

The rapid expansion of the Internet of Things (IoT) poses challenges to certain applications, such as Digital Twin (DT). While data from user devices can be filtered by human intelligence, this is not feasible for IoT devices. Owing to the voluminous data generated by IoT devices that require transmission and computing, traditional cloud computing architectures may no longer guarantee the Quality of Experience (QoE), even causing network congestion. To address this issue, we propose a novel Cloud-Network-Edge-Terminal (CNET) model, which includes an intelligent edge layer for filtering IoT data. The computing paradigm shift indicates that the network will provide services at the edge rather than in the cloud, which is so-called service localization. To demonstrate the benefits of service localization, we use integrated user requirement descriptions to measure QoE, specifically the concepts of Service Requirement Zone (SRZ) and User Satisfaction Ratio (USR). Additionally, we conduct extensive numerical simulations to evaluate the model’s performance under varying Degrees of Localization (DoL). Our results show that service localization can significantly improve USR even in changing network conditions.

关键词： Location awareness Cloud computing Computational modeling Human intelligence Numerical simulation Information filters Numerical models

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